Mateus Valverde Gasparino — accepted AI-conference papers

2024

Demonstrating CropFollow++: Robust Under-Canopy Navigation with Keypoints

Arun Narenthiran Sivakumar, Mateus Valverde Gasparino, Michael McGuire, Vitor Akihiro Hisano Higuti, M. Ugur Akcal, Girish Chowdhary

RSS 2024poster

We present an empirically robust vision-based navigation system for under-canopy agricultural robots using semantic keypoints. Autonomous under-canopy navigation is challenging due to the tight spacing between the crop rows (∼ 0.75 m), degradation in RTK-GPS accuracy due to multipath error, and nois…

Cited by 3 Source ↗PDF ↗Scholar ↗

2024

Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning for Autonomous Visual Robot Navigation

Shreya Gummadi, Mateus Valverde Gasparino, Deepak Vasisht, Girish Chowdhary

RA-L 2024

Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single

Cited by 9 Source ↗Scholar ↗

2024

W-RIZZ: A Weakly-Supervised Framework for Relative Traversability Estimation in Mobile Robotics

Andre Schreiber, Arun Narenthiran Sivakumar, Peter Du, Mateus Valverde Gasparino, Girish Chowdhary, Katherine Rose Driggs-Campbell

RA-L 2024

Successful deployment of mobile robots in unstructured domains requires an understanding of the environment and terrain to avoid hazardous areas, getting stuck, and colliding with obstacles. Traversability estimation–which predicts where in the environment a robot can travel–is one prominent approac

Cited by 6 Source ↗code ↗Scholar ↗

2022

WayFAST: Navigation With Predictive Traversability in the Field

Mateus Valverde Gasparino, Arun Narenthiran Sivakumar, Yixiao Liu, Andres E. Baquero Velasquez, Vitor A. H. Higuti, John G. Rogers +2

RA-L 2022

We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonom

Cited by 73 Source ↗code ↗Scholar ↗

2021

Learned Visual Navigation for Under-Canopy Agricultural Robots

Arun Narenthiran Sivakumar, Sahil Modi, Mateus Valverde Gasparino, Che Ellis, Andres Eduardo Baquero Velasquez, Girish Chowdhary +1

RSS 2021poster

This paper describes a system for visually guided autonomous navigation of under-canopy farm robots. Low-cost under-canopy robots can drive between crop rows under the plant canopy and accomplish tasks that are infeasible for over-the-canopy drones or larger agricultural equipment. However; autonomo…

Cited by 76 Source ↗PDF ↗Scholar ↗